Interesting Hypotheses: Hypothesis 1: The pictures are FAKE

A directional and causal hypothesis is the most specific of those discussed so far.

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The Natural Order hypothesis is based on research findings (Dulay & Burt, 1974; Fathman, 1975; Makino, 1980 cited in Krashen, 1987) which suggested that the acquisition of grammatical structures follows a 'natural order' which is predictable. For a given language, some grammatical structures tend to be acquired early while others late. This order seemed to be independent of the learners' age, L1 background, conditions of exposure, and although the agreement between individual acquirers was not always 100% in the studies, there were statistically significant similarities that reinforced the existence of a Natural Order of language acquisition. Krashen however points out that the implication of the natural order hypothesis is not that a language program syllabus should be based on the order found in the studies. In fact, he rejects grammatical sequencing when the goal is language acquisition.

The Logtime hypothesis probably applies to all timeintervals, not just years and seasons.

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In fact, the standard way to treat food intolerance is to remove the offending items from the patient's diet.

A suggested interpretation of exorphin research

But what are the effects of these foods on normal people?

Often, the people who claim to avoid hypothesis testing will say something like "the 95% confidence interval of 25.9 to 47.4% does not include 50%, so we conclude that the plant extract significantly changed the sex ratio." This is a clumsy and roundabout form of hypothesis testing, and they might as well admit it and report the P value.

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You should decide whether to use the one-tailed or two-tailed probability before you collect your data, of course. A one-tailed probability is more powerful, in the sense of having a lower chance of false negatives, but you should only use a one-tailed probability if you really, truly have a firm prediction about which direction of deviation you would consider interesting. In the chicken example, you might be tempted to use a one-tailed probability, because you're only looking for treatments that decrease the proportion of worthless male chickens. But if you accidentally found a treatment that produced 87% male chickens, would you really publish the result as "The treatment did not cause a significant decrease in the proportion of male chickens"? I hope not. You'd realize that this unexpected result, even though it wasn't what you and your farmer friends wanted, would be very interesting to other people; by leading to discoveries about the fundamental biology of sex-determination in chickens, in might even help you produce more female chickens someday. Any time a deviation in either direction would be interesting, you should use the two-tailed probability. In addition, people are skeptical of one-tailed probabilities, especially if a one-tailed probability is significant and a two-tailed probability would not be significant (as in our chocolate-eating chicken example). Unless you provide a very convincing explanation, people may think you decided to use the one-tailed probability after you saw that the two-tailed probability wasn't quite significant, which would be cheating. It may be easier to always use two-tailed probabilities. For this handbook, I will always use two-tailed probabilities, unless I make it very clear that only one direction of deviation from the null hypothesis would be interesting.

An Interesting Hypothesis - xkcd

(Six-decibel steps very nearly doublevoltage or current, quadrupling power.) For an even finer resolution, we can use thirty steps per factor of 10: Using forty steps per factor of 10: Finally, using fifty steps per factor of 10, for an exponential growth of about 4.7% per step: On any of the above tables, compare the spacing in thepreteen years (moving the decimal point where required) and in themiddle years with the spacing in the teens and twenties: how stretchedout is childhood, and how compressed is middle age!

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You must choose your significance level before you collect the data, of course. If you choose to use a different significance level than the conventional 0.05, people will be skeptical; you must be able to justify your choice. Throughout this handbook, I will always use P If you are doing an experiment where the cost of a false positive is a lot greater or smaller than the cost of a false negative, or an experiment where you think it is unlikely that the alternative hypothesis will be true, you should consider using a different significance level.